scispace - formally typeset
Search or ask a question

Dominating Set with Metaheuristic Algorithms 


Best insight from top research papers

Metaheuristic algorithms for the Dominating Set problem have not been specifically mentioned in the provided abstracts. The abstracts focus on the complexity and parameterized algorithms for the Dominating Set problem, as well as its variations such as Upper Dominating Set and Mixed Dominating Set. The abstracts discuss the PSPACE-completeness of the problem, linear-time algorithms for certain graph classes, and the parameterized complexity of the problem. However, there is no mention of metaheuristic algorithms or their application to the Dominating Set problem.

Answers from top 5 papers

More filters
Papers (5)Insight
The paper does not discuss Dominating Set with metaheuristic algorithms. The paper focuses on the Upper Dominating Set problem and its complexity analysis.
The provided paper does not discuss dominating set with metaheuristic algorithms. The paper focuses on self-stabilizing algorithms for dominating sets in graphs.
The provided paper does not discuss metaheuristic algorithms for the Dominating Set problem.
The provided paper does not discuss dominating set with metaheuristic algorithms.
The provided paper does not discuss metaheuristic algorithms for the Dominating Set problem.

Related Questions

How does the group learning algorithm differ from other metaheuristic algorithms?5 answersThe group learning algorithm, as seen in the Group Learning based Optimization (GLBO) Algorithm and the Group Teaching Optimization Algorithm (GTOA), stands out from other metaheuristic algorithms by incorporating strategies inspired by educational principles to optimize solutions. These algorithms divide populations into groups based on characteristics like scores or learning motivations, implementing tailored strategies for each subgroup to enhance overall performance. Additionally, the GLMPA algorithm introduces a group learning strategy to diversify populations and escape local optima, further distinguishing it from traditional metaheuristic approaches. In contrast to standard metaheuristic algorithms that may not consider such group dynamics, these group learning algorithms leverage group behaviors and individual characteristics to improve optimization outcomes.
What are the metaheuristics used for multi-objective problems?5 answersMetaheuristic algorithms commonly used for multi-objective problems include Multi-Objective Artificial Bee Colony (MOABC), Non-Dominant Sorting Genetic Algorithm II (NSGA-II), Multiobjective Electric Fish Optimization (MOEFO), and Tabu Search, Genetic Algorithm, and Simulated Annealing. These algorithms are designed to efficiently solve complex multi-objective optimization problems by balancing multiple conflicting objectives. MOABC and NSGA-II have shown strong performance in terms of convergence and robustness, making them suitable for multi-objective problems. MOEFO, based on the Electric Fish Optimization algorithm, has demonstrated effectiveness in solving MOOPs efficiently, especially for Many-objective optimization problems. Additionally, Tabu Search, Genetic Algorithm, and Simulated Annealing have been developed to find near-optimal global solutions for large-scale multi-objective supply chain network design problems.
What does is mean dimension of state space in metaheuristic algorithms?5 answersThe dimension of the state space in metaheuristic algorithms refers to the number of variables or components that define the state of the algorithm. It represents the size or complexity of the problem that the algorithm is trying to solve. In metaheuristic algorithms, the state space dimension is an important factor as it affects the search space and the computational complexity of the algorithm. Different metaheuristic algorithms may have different ways of representing and manipulating the state space, but the dimensionality of the state space remains a fundamental aspect.
Domination in graph theory5 answersThe concept of domination in graph theory refers to the study of dominating sets, which are subsets of vertices in a graph such that every vertex not in the set is adjacent to at least one vertex in the set. The domination number of a graph is the minimum cardinality of a dominating set, while the paired domination number is the minimum cardinality of a dominating set that induces a subgraph containing a perfect matching. Domination theory has connections with other fundamental graph classes such as median graphs and partial cubes. The study of domination and independence numbers in graph theory has applications in various fields, including computing, social sciences, and natural sciences.
Domination in Graph theory Research Topics5 answersGraph theory research topics in domination include the study of domination and independence numbers, the concept of Fibonacci numbers of graphs, variations of graph domination involving partitions of the vertex set, and exploration of domination-related parameters in generalized lexicographic products of graphs. Domination sets and minimal dominating sets are also important in applications of graph theory. Additionally, domination parameters such as the domination number, total domination number, and signed domination number have been studied in spectral graph theory.
Is there any papers on drone delivery using metaheuristic?5 answersThe challenging idea of using drones in last-mile delivery systems has led to the development of several papers on drone delivery using metaheuristic algorithms. One paper by Çakmak and Bulkan proposes a hybrid metaheuristic solution method for the traveling salesman problem with drone (TSP-D) using a combination of genetic algorithm and ant colony optimization algorithm. Another paper by Jana and Mandal introduces a fully polynomial time approximation scheme (FPTAS) for the single drone delivery scheduling problem (SDSP) and a 1/4-approximation algorithm for the multiple drone delivery scheduling problem (MDSP). Additionally, Mandal proposes a fully polynomial time approximation scheme (FPTAS) for SDSP and a 1/3-approximation algorithm for MDSP with a constraint on the number of drones. These papers provide valuable insights into the use of metaheuristics for optimizing drone delivery systems.